使用Google AutoML Tables多分类遇异常：仅输出40个高频类概率求助

Hey Yassine, let’s figure out why your Google AutoML Tables model is only outputting probabilities for 40 high-frequency classes instead of all 110 in your multiclass task. Here are the most common causes and fixes to check:

AutoML Tables prioritizes classes it can actually learn meaningful patterns from. If many of your 110 classes have extremely few samples (like single-digit counts), the model will likely ignore them entirely—there’s just not enough data to train a reliable predictor for those classes.

• Fix:
  • Audit your training data to count samples per class (you can use a simple pandas query like df['label_column'].value_counts() for this).
  • For underrepresented classes, collect more labeled data if possible. If that’s not feasible, consider merging similar classes or enabling class weighting in AutoML’s training settings to force the model to pay more attention to small classes.

AutoML Tables has a default setting that filters out classes with very low sample counts to avoid wasting compute on unlearnable categories. You might have this enabled without realizing it.

• Fix:
  • Head to your AutoML Tables training job configuration. Look for settings related to "class filtering" or "minimum samples per class".
  • Adjust the threshold to include all 110 classes (or disable the filter entirely) if your low-frequency classes have at least a handful of samples to work with.

Sometimes the model can actually predict all classes, but your prediction request is set to only return the top N highest-probability classes. If you’re using the API, this is a common oversight.

• Fix:
  • When making prediction calls, ensure you’re using the parameter to return all class probabilities. For example, in the REST API, add return_all_classes: true to your request body. If using the Python client, set return_all_classes=True in the predict method.

It’s possible some class labels were accidentally filtered out during data import due to formatting errors, missing values, or duplicate label variations.

• Fix:
  • Verify your label column for consistency: check for typos, case sensitivity (e.g., "ClassA" vs "classA"), or missing values that might have excluded entire classes from the training set.
  • Double-check the data import logs in AutoML to confirm all 110 classes were successfully ingested.

Start with auditing your class sample distribution and AutoML’s filtering settings—these are the most likely culprits. If you still run into issues, dig into the data import details and prediction request parameters.

内容的提问来源于stack exchange，提问作者Yass Light